Deep transfer learning for clinical decision-making based on high-throughput data : comprehensive survey with benchmark results

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Article numberbbad254
Journal / PublicationBriefings in Bioinformatics
Volume24
Issue number4
Online published14 Jul 2023
Publication statusPublished - Jul 2023

Abstract

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization. © The Author(s) 2023. Published by Oxford University Press. All rights reserved.

Research Area(s)

  • transfer learning, domain adaptation, health informatics, clinical decision-making, cross-species methods

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. / Toseef, Muhammad; Petinrin, Olutomilayo Olayemi; Wang, Fuzhou et al.
In: Briefings in Bioinformatics, Vol. 24, No. 4, bbad254, 07.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review